We compare GraphTrailer against several unsupervised approaches
(first block in Table REF ) including:
Random selection among all shots and among TPs identified by
GraphTrailer; we also implement two graph-based systems based on a
fully-connected graph, where nodes are shots and edges denote the
degree of similarity between them. This graph has no knowledge of TPs,
it is constructed by calculating the similarity between generic
multimodal
representations. TextRank [1]}
operates over this graph to select shots based on their centrality,
while GraphTrailer without TPs traverses the graph with TP
and sentiment criteria removed (Equation REF ). For the unsupervised systems which include stochasticity and produce proposals (Random, GraphTrailer), we consider the best proposal trailer. The second
block of Table REF  presents supervised
approaches which use noisy trailer labels for training. These include
CCANet [2]}, which only considers visual
information and computes the cross-attention between movie and trailer
shots, and a vanilla Transformer trained for the binary task
of identifying whether a shot should be in the trailer without
considering screenplays, sentiment or TPs.
Supervised
GraphTrailer consists of our video-based network trained on the
same data as the Transformer.
